OwlSight: A Panoramic Monitor for Chain-of-Thought Reasoning· OwlSight：链式思考的全景监控
Chain-of-Thought (CoT) monitoring offers actionable evidence for oversight AI systems, with the potential to enable early detection and intervention. However, existing approaches are focused on specific tasks, whereas the regulation of AI, particularly reasoning models, requires monitoring a broader range of tasks. We introduce Factor for CoT monitoring, a strategy that supports customizing monitoring requirements in the prompt, which enables a single monitor to cover different tasks by relying on different factors. Furthermore, we introduce two types of factors, fine-grained and coarse-grained (e.g., monitoring whether a specific email address has been leaked versus monitoring privacy leakage), to accommodate diverse user monitoring needs. Based on this, we develop a CoT monitor named Owl
OwlSight 提供链式思考的全景监控，支持多样任务监测需求。
- 核心方法
- 提出基于因素的链式思考监控策略，允许在提示中定制监控要求，支持细粒度和粗粒度两种因素类型来满足不同的用户需求。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有的链式思考监控方法仅专注于特定任务，不满足全面监管AI系统（尤其是推理模型）的需求。
- 关键实验
- 未提供
- 主要贡献
- 开发了OwlSight，一个能够覆盖多种任务的链式思考监控工具，提供早期检测与介入的可能性。
- 意义与局限
- 提升了AI系统的透明度和可控性，有助于构建信任但存在适用范围和精度的限制。